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LLM-based multi-agent systems (MASs) are increasingly used to solve complex tasks through coordinated reasoning, tool use, and interaction with external resources. However, attributing failures in such systems remains challenging because the observed outcome often does not directly reveal the error responsible for the...
Embodied Agents System (EAS) are increasingly deployed in open-world physical domains, where reliability directly dictates deployment quality and human-agent trust. However, existing evaluations rely on outcome-centric metrics as success rate or safety scores that collapse diverse execution trajectories into coarse sco...
Root cause analysis (RCA) in microservice systems is challenging because observable symptoms often propagate along service dependencies and become separated from the actual underlying cause. Recent large language model (LLM)-based agent frameworks have shown promise for automating RCA, but existing methods still suffer...
This work proposes a requirement-oriented normalization method using controlled natural language and large-language-model-based prompt engineering to strengthen semantic representation and mitigate semantic divergence in source code requirements.
A novel benchmark for evaluating the task-solving capabilities of LLM agents under dynamic toolset evolution, and proposes 11 mutation operators to simulate realistic tool evolution within 123 MCP servers, establishing MCPEvol-Bench as a standard for evaluating agent adaptability in dynamic tool environments.
ReFrame is a training-free multimodal input reframing framework where two agents share a lightweight locally deployed MLLM: the evidence-generation agent constructs complementary risk and utility evidence, and the rewrite-and-routing agent converts it into a safe proxy prompt and image-routing decision before calling t...
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